7 papers
Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
Philipp Dahlinger, Balázs Gyenes, Niklas Freymuth +6
Graph Network Simulators (GNSs) have emerged as powerful surrogates for complex physics-based simulation, offering inherent differentiability and orders-of-magnitude speedups over…
Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards
Niklas Freymuth, Philipp Dahlinger, Tobias Würth +3
Simulating physical systems is essential in engineering, but analytical solutions are limited to straightforward problems. Consequently, numerical methods like the Finite Element M…
Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning
Philipp Dahlinger, Niklas Freymuth, Tai Hoang +4
Simulating object deformations is a critical challenge across many scientific domains, including robotics, manufacturing, and structural mechanics. Learned Graph Network Simulators…
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Philipp Dahlinger, Tai Hoang, Denis Blessing +2
Accurately simulating physics is crucial across scientific domains, with applications spanning from robotics to materials science. While traditional mesh-based simulations are prec…
AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction
Niklas Freymuth, Tobias Würth, Nicolas Schreiber +9
The cost and accuracy of simulating complex physical systems using the Finite Element Method (FEM) scales with the resolution of the underlying mesh. Adaptive meshes improve comput…
Beyond Task Performance: Human Experience in Human-Robot Collaboration
Sean Kille, Jan Heinrich Robens, Philipp Dahlinger +8
Human interaction experience plays a crucial role in the effectiveness of human-machine collaboration, especially as interactions in future systems progress towards tighter physica…